1. Field of the Invention
The present invention relates to the field of network traffic generation and more particularly to the field of network traffic generation for different scales of operation.
2. Description of the Related Art
Traffic generation for packet switched networks refers to the injection of artificially generated packet streams into a target network with a traffic pattern according to a stochastic specification or trace. Typically, traffic generation supports the testing of a network application or an underlying network under exemplary load conditions to determine application or network response characteristics to the load. Synthetic traffic generation based upon a stochastic specification enables the creation of arbitrary workloads for a network, while trace-based traffic generation allows for the reproduction of a known traffic pattern. Notably, traffic generation facilitates the testing and study of the performance characteristics of a network application or system without incurring the substantial cost of hard and soft equipment and human testers that otherwise would be required.
Network traffic is known to be bursty in nature. Traffic burstiness has been defined as the tendency of data packets to arrive in bursts, with the inter-packet arrival time within the burst being much smaller than the average inter-packet arrival time. Bursty traffic can have a significant effect on the queuing delays and response times of a network application or network system. As such, an appropriate measure of burstiness can serve as an important traffic parameter describing the variability in load intensity and packet arrival rate in a network system.
Bursty network traffic historically has been characterized as Poisson distributed and the modeling, analysis and the design parameters of traffic generation systems tend to abide by this assumption. Notwithstanding, recent studies indicate that network traffic not only is bursty in nature, but also network traffic has been found to be self-similar in nature. Self-similarity as described in the seminal paper, Leland, Taqqu, Willinger and Wilson, On the Self-Similar Nature of Ethernet Traffic, in IEEE/ACM Transactions on Networking, vol. 2, no. 1 (February 1994), is a process displaying structural similarities across a wide range of scales of a specific dimension. In other words, the reference structure repeats itself over a wide range of scales of diverse dimensions (geometrical, or statistical, or temporal), and the statistics of the process do not change with the change.
Burstiness in self-similar network traffic most often has been modeled according to a Markov modulated Poisson process (MMPP). The MMPP model describes two exponentially distributed states: idle and bursty. The transition from the idle state to the bursty state in the MMPP model depends upon the size of the required bursts which can be constant or exponentially distributed. To produce network traffic according to the MMPP model, one need only specify three parameters: interpacket gap (IPG), packet size and the size of the burst. A fourth, optional parameter can be specified as a “spread” to increase the variability of the generated data as it is well known in the art.